Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations11991
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory112.0 B

Variable types

Numeric8
Text3
Categorical2
DateTime1

Alerts

EmployeeNumber is highly overall correlated with originHigh correlation
origin is highly overall correlated with EmployeeNumberHigh correlation
priceEach is highly overall correlated with sales_amountHigh correlation
quantityOrdered is highly overall correlated with sales_amountHigh correlation
sales_amount is highly overall correlated with priceEach and 1 other fieldsHigh correlation
status is highly imbalanced (76.3%) Imbalance
origin is highly imbalanced (77.8%) Imbalance
EmployeeNumber has 428 (3.6%) zeros Zeros

Reproduction

Analysis started2025-02-07 11:54:23.485833
Analysis finished2025-02-07 11:54:31.954446
Duration8.47 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

orderNumber
Real number (ℝ)

Distinct326
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10268.808
Minimum10100
Maximum10425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:32.050855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10116
Q110182
median10273
Q310358
95-th percentile10409
Maximum10425
Range325
Interquartile range (IQR)176

Descriptive statistics

Standard deviation96.897075
Coefficient of variation (CV)0.0094360589
Kurtosis-1.3122306
Mean10268.808
Median Absolute Deviation (MAD)87
Skewness-0.12819144
Sum1.2313327 × 108
Variance9389.0432
MonotonicityIncreasing
2025-02-07T12:54:32.194479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10386 234
 
2.0%
10350 221
 
1.8%
10262 208
 
1.7%
10212 208
 
1.7%
10358 182
 
1.5%
10104 169
 
1.4%
10380 169
 
1.4%
10383 169
 
1.4%
10153 169
 
1.4%
10182 153
 
1.3%
Other values (316) 10109
84.3%
ValueCountFrequency (%)
10100 12
 
0.1%
10101 16
 
0.1%
10102 6
 
0.1%
10103 64
 
0.5%
10104 169
1.4%
10105 60
 
0.5%
10106 45
 
0.4%
10107 18
 
0.2%
10108 42
 
0.4%
10109 15
 
0.1%
ValueCountFrequency (%)
10425 39
0.3%
10424 78
0.7%
10423 10
 
0.1%
10422 4
 
< 0.1%
10421 18
 
0.2%
10420 39
0.3%
10419 42
0.4%
10418 18
 
0.2%
10417 78
0.7%
10416 28
 
0.2%

orderLineNumber
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4966225
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:32.314019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile14
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2139568
Coefficient of variation (CV)0.6486381
Kurtosis-0.55744911
Mean6.4966225
Median Absolute Deviation (MAD)3
Skewness0.58126728
Sum77901
Variance17.757432
MonotonicityNot monotonic
2025-02-07T12:54:32.427814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1274
10.6%
2 1222
10.2%
3 1139
9.5%
4 1089
9.1%
5 1009
8.4%
6 942
7.9%
7 841
 
7.0%
8 803
 
6.7%
9 714
 
6.0%
10 629
 
5.2%
Other values (8) 2329
19.4%
ValueCountFrequency (%)
1 1274
10.6%
2 1222
10.2%
3 1139
9.5%
4 1089
9.1%
5 1009
8.4%
6 942
7.9%
7 841
7.0%
8 803
6.7%
9 714
6.0%
10 629
5.2%
ValueCountFrequency (%)
18 42
 
0.4%
17 110
 
0.9%
16 188
 
1.6%
15 234
 
2.0%
14 312
2.6%
13 421
3.5%
12 459
3.8%
11 563
4.7%
10 629
5.2%
9 714
6.0%

customerNumber
Real number (ℝ)

Distinct98
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.96289
Minimum103
Maximum496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:32.615242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile121
Q1141
median145
Q3298
95-th percentile456
Maximum496
Range393
Interquartile range (IQR)157

Descriptive statistics

Standard deviation111.08194
Coefficient of variation (CV)0.51674937
Kurtosis-0.15746026
Mean214.96289
Median Absolute Deviation (MAD)21
Skewness1.0877225
Sum2577620
Variance12339.197
MonotonicityNot monotonic
2025-02-07T12:54:32.761551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141 3367
28.1%
124 1620
 
13.5%
114 220
 
1.8%
151 192
 
1.6%
276 184
 
1.5%
323 184
 
1.5%
148 172
 
1.4%
353 164
 
1.4%
119 159
 
1.3%
187 153
 
1.3%
Other values (88) 5576
46.5%
ValueCountFrequency (%)
103 21
 
0.2%
112 87
 
0.7%
114 220
 
1.8%
119 159
 
1.3%
121 128
 
1.1%
124 1620
13.5%
128 88
 
0.7%
129 63
 
0.5%
131 141
 
1.2%
141 3367
28.1%
ValueCountFrequency (%)
496 144
1.2%
495 36
 
0.3%
489 24
 
0.2%
487 30
 
0.3%
486 66
0.6%
484 30
 
0.3%
475 26
 
0.2%
473 16
 
0.1%
471 46
 
0.4%
462 78
0.7%

EmployeeNumber
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1313.1542
Minimum0
Maximum1702
Zeros428
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:32.896265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1165
Q11216
median1370
Q31401
95-th percentile1612
Maximum1702
Range1702
Interquartile range (IQR)185

Descriptive statistics

Standard deviation289.29432
Coefficient of variation (CV)0.22030491
Kurtosis12.382166
Mean1313.1542
Median Absolute Deviation (MAD)84
Skewness-3.1706246
Sum15746032
Variance83691.203
MonotonicityNot monotonic
2025-02-07T12:54:33.015514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1370 3740
31.2%
1165 1971
16.4%
1401 726
 
6.1%
1501 641
 
5.3%
1611 633
 
5.3%
1337 572
 
4.8%
1612 546
 
4.6%
1504 534
 
4.5%
1323 529
 
4.4%
1286 447
 
3.7%
Other values (5) 1652
13.8%
ValueCountFrequency (%)
0 428
 
3.6%
1165 1971
16.4%
1166 262
 
2.2%
1188 307
 
2.6%
1216 372
 
3.1%
1286 447
 
3.7%
1323 529
 
4.4%
1337 572
 
4.8%
1370 3740
31.2%
1401 726
 
6.1%
ValueCountFrequency (%)
1702 283
 
2.4%
1612 546
 
4.6%
1611 633
 
5.3%
1504 534
 
4.5%
1501 641
 
5.3%
1401 726
 
6.1%
1370 3740
31.2%
1337 572
 
4.8%
1323 529
 
4.4%
1286 447
 
3.7%
Distinct109
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:33.315302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1014928
Min length8

Characters and Unicode

Total characters97145
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS24_3969
2nd rowS24_3969
3rd rowS24_3969
4th rowS18_2248
5th rowS18_2248
ValueCountFrequency (%)
s18_3232 242
 
2.0%
s24_2840 164
 
1.4%
s24_1444 159
 
1.3%
s32_2509 156
 
1.3%
s50_1392 150
 
1.3%
s24_4048 147
 
1.2%
s18_2238 146
 
1.2%
s12_4473 145
 
1.2%
s18_2319 143
 
1.2%
s32_3207 137
 
1.1%
Other values (99) 10402
86.7%
2025-02-07T12:54:33.740332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13986
14.4%
S 11991
12.3%
_ 11991
12.3%
1 11909
12.3%
4 8871
9.1%
8 8555
8.8%
3 7519
7.7%
0 7276
7.5%
7 4393
 
4.5%
9 3925
 
4.0%
Other values (2) 6729
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 13986
14.4%
S 11991
12.3%
_ 11991
12.3%
1 11909
12.3%
4 8871
9.1%
8 8555
8.8%
3 7519
7.7%
0 7276
7.5%
7 4393
 
4.5%
9 3925
 
4.0%
Other values (2) 6729
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 13986
14.4%
S 11991
12.3%
_ 11991
12.3%
1 11909
12.3%
4 8871
9.1%
8 8555
8.8%
3 7519
7.7%
0 7276
7.5%
7 4393
 
4.5%
9 3925
 
4.0%
Other values (2) 6729
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 13986
14.4%
S 11991
12.3%
_ 11991
12.3%
1 11909
12.3%
4 8871
9.1%
8 8555
8.8%
3 7519
7.7%
0 7276
7.5%
7 4393
 
4.5%
9 3925
 
4.0%
Other values (2) 6729
6.9%

status
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
Shipped
10941 
Cancelled
 
357
Resolved
 
329
In Process
 
188
Disputed
 
100

Length

Max length10
Median length7
Mean length7.1423568
Min length7

Characters and Unicode

Total characters85644
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShipped
2nd rowShipped
3rd rowShipped
4th rowShipped
5th rowShipped

Common Values

ValueCountFrequency (%)
Shipped 10941
91.2%
Cancelled 357
 
3.0%
Resolved 329
 
2.7%
In Process 188
 
1.6%
Disputed 100
 
0.8%
On Hold 76
 
0.6%

Length

2025-02-07T12:54:33.891204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T12:54:33.998930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
shipped 10941
89.3%
cancelled 357
 
2.9%
resolved 329
 
2.7%
in 188
 
1.5%
process 188
 
1.5%
disputed 100
 
0.8%
on 76
 
0.6%
hold 76
 
0.6%

Most occurring characters

ValueCountFrequency (%)
p 21982
25.7%
e 12601
14.7%
d 11803
13.8%
i 11041
12.9%
S 10941
12.8%
h 10941
12.8%
l 1119
 
1.3%
s 805
 
0.9%
n 621
 
0.7%
o 593
 
0.7%
Other values (14) 3197
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 21982
25.7%
e 12601
14.7%
d 11803
13.8%
i 11041
12.9%
S 10941
12.8%
h 10941
12.8%
l 1119
 
1.3%
s 805
 
0.9%
n 621
 
0.7%
o 593
 
0.7%
Other values (14) 3197
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 21982
25.7%
e 12601
14.7%
d 11803
13.8%
i 11041
12.9%
S 10941
12.8%
h 10941
12.8%
l 1119
 
1.3%
s 805
 
0.9%
n 621
 
0.7%
o 593
 
0.7%
Other values (14) 3197
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 21982
25.7%
e 12601
14.7%
d 11803
13.8%
i 11041
12.9%
S 10941
12.8%
h 10941
12.8%
l 1119
 
1.3%
s 805
 
0.9%
n 621
 
0.7%
o 593
 
0.7%
Other values (14) 3197
 
3.7%

quantityOrdered
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.371362
Minimum6
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:34.129677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21
Q127
median35
Q343
95-th percentile49
Maximum97
Range91
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6569487
Coefficient of variation (CV)0.27301603
Kurtosis0.39201569
Mean35.371362
Median Absolute Deviation (MAD)8
Skewness0.35269445
Sum424138
Variance93.256658
MonotonicityNot monotonic
2025-02-07T12:54:34.279577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 488
 
4.1%
34 464
 
3.9%
49 451
 
3.8%
32 448
 
3.7%
44 432
 
3.6%
33 430
 
3.6%
20 421
 
3.5%
31 419
 
3.5%
29 417
 
3.5%
46 416
 
3.5%
Other values (51) 7605
63.4%
ValueCountFrequency (%)
6 4
 
< 0.1%
10 7
 
0.1%
11 5
 
< 0.1%
12 3
 
< 0.1%
13 1
 
< 0.1%
15 12
 
0.1%
16 2
 
< 0.1%
18 7
 
0.1%
19 16
 
0.1%
20 421
3.5%
ValueCountFrequency (%)
97 3
 
< 0.1%
90 4
 
< 0.1%
85 2
 
< 0.1%
77 6
 
0.1%
76 5
 
< 0.1%
70 16
0.1%
66 21
0.2%
65 6
 
0.1%
64 8
 
0.1%
62 2
 
< 0.1%

priceEach
Real number (ℝ)

High correlation 

Distinct1572
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.502459
Minimum26.55
Maximum214.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:34.397921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26.55
5-th percentile37.38
Q160.94
median85.87
Q3115.03
95-th percentile157.49
Maximum214.3
Range187.75
Interquartile range (IQR)54.09

Descriptive statistics

Standard deviation36.7853
Coefficient of variation (CV)0.40645636
Kurtosis-0.04781738
Mean90.502459
Median Absolute Deviation (MAD)26.87
Skewness0.58830573
Sum1085215
Variance1353.1583
MonotonicityNot monotonic
2025-02-07T12:54:34.542836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.48 50
 
0.4%
77.24 43
 
0.4%
66.99 40
 
0.3%
111.57 36
 
0.3%
43.27 35
 
0.3%
157.49 35
 
0.3%
48.8 34
 
0.3%
84.33 33
 
0.3%
77.05 32
 
0.3%
60.3 32
 
0.3%
Other values (1562) 11621
96.9%
ValueCountFrequency (%)
26.55 7
 
0.1%
27.22 3
 
< 0.1%
27.55 2
 
< 0.1%
27.88 28
0.2%
28.64 12
0.1%
28.88 7
 
0.1%
29.21 2
 
< 0.1%
29.35 6
 
0.1%
29.54 3
 
< 0.1%
29.87 21
0.2%
ValueCountFrequency (%)
214.3 15
0.1%
212.16 3
 
< 0.1%
210.01 2
 
< 0.1%
207.87 3
 
< 0.1%
207.8 3
 
< 0.1%
205.73 10
0.1%
205.72 7
0.1%
203.64 2
 
< 0.1%
203.59 5
 
< 0.1%
201.57 16
0.1%

sales_amount
Real number (ℝ)

High correlation 

Distinct2878
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3210.0436
Minimum481.5
Maximum11503.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:34.679818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum481.5
5-th percentile1157.2
Q11988.2
median2866.2
Q34091.34
95-th percentile6366
Maximum11503.14
Range11021.64
Interquartile range (IQR)2103.14

Descriptive statistics

Standard deviation1641.1488
Coefficient of variation (CV)0.51125437
Kurtosis1.4735427
Mean3210.0436
Median Absolute Deviation (MAD)1009.32
Skewness1.1035407
Sum38491632
Variance2693369.4
MonotonicityNot monotonic
2025-02-07T12:54:34.813222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4295.04 26
 
0.2%
5978.98 22
 
0.2%
1818.25 22
 
0.2%
3390.2 22
 
0.2%
3314.44 22
 
0.2%
4011.5 18
 
0.2%
3986.4 17
 
0.1%
3142.8 17
 
0.1%
4529.6 17
 
0.1%
2273.92 17
 
0.1%
Other values (2868) 11791
98.3%
ValueCountFrequency (%)
481.5 3
< 0.1%
529.35 3
< 0.1%
531 3
< 0.1%
546.66 1
 
< 0.1%
553.52 3
< 0.1%
557.6 3
< 0.1%
577.6 3
< 0.1%
597.4 2
< 0.1%
615 4
< 0.1%
625.5 3
< 0.1%
ValueCountFrequency (%)
11503.14 2
 
< 0.1%
11170.52 3
 
< 0.1%
10723.6 1
 
< 0.1%
10460.16 4
 
< 0.1%
10286.4 9
0.1%
10072 13
0.1%
9974.4 3
 
< 0.1%
9712.04 3
 
< 0.1%
9571.08 2
 
< 0.1%
9568.73 2
 
< 0.1%

origin
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
spain
11563 
japan
 
428

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters59955
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspain
2nd rowspain
3rd rowspain
4th rowspain
5th rowspain

Common Values

ValueCountFrequency (%)
spain 11563
96.4%
japan 428
 
3.6%

Length

2025-02-07T12:54:34.940966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T12:54:35.047688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
spain 11563
96.4%
japan 428
 
3.6%

Most occurring characters

ValueCountFrequency (%)
a 12419
20.7%
p 11991
20.0%
n 11991
20.0%
s 11563
19.3%
i 11563
19.3%
j 428
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12419
20.7%
p 11991
20.0%
n 11991
20.0%
s 11563
19.3%
i 11563
19.3%
j 428
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12419
20.7%
p 11991
20.0%
n 11991
20.0%
s 11563
19.3%
i 11563
19.3%
j 428
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12419
20.7%
p 11991
20.0%
n 11991
20.0%
s 11563
19.3%
i 11563
19.3%
j 428
 
0.7%
Distinct2988
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:35.345369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.246685
Min length7

Characters and Unicode

Total characters86895
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)0.8%

Sample

1st row10100-1
2nd row10100-1
3rd row10100-1
4th row10100-2
5th row10100-2
ValueCountFrequency (%)
10153-7 13
 
0.1%
10424-4 13
 
0.1%
10424-6 13
 
0.1%
10205-3 13
 
0.1%
10205-5 13
 
0.1%
10153-10 13
 
0.1%
10279-4 13
 
0.1%
10104-6 13
 
0.1%
10104-5 13
 
0.1%
10104-4 13
 
0.1%
Other values (2978) 11861
98.9%
2025-02-07T12:54:36.022344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22914
26.4%
0 15220
17.5%
- 11991
13.8%
3 8350
 
9.6%
2 7889
 
9.1%
4 4403
 
5.1%
5 3769
 
4.3%
8 3528
 
4.1%
6 3152
 
3.6%
7 2997
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22914
26.4%
0 15220
17.5%
- 11991
13.8%
3 8350
 
9.6%
2 7889
 
9.1%
4 4403
 
5.1%
5 3769
 
4.3%
8 3528
 
4.1%
6 3152
 
3.6%
7 2997
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22914
26.4%
0 15220
17.5%
- 11991
13.8%
3 8350
 
9.6%
2 7889
 
9.1%
4 4403
 
5.1%
5 3769
 
4.3%
8 3528
 
4.1%
6 3152
 
3.6%
7 2997
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22914
26.4%
0 15220
17.5%
- 11991
13.8%
3 8350
 
9.6%
2 7889
 
9.1%
4 4403
 
5.1%
5 3769
 
4.3%
8 3528
 
4.1%
6 3152
 
3.6%
7 2997
 
3.4%
Distinct273
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:36.321584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.8369611
Min length7

Characters and Unicode

Total characters93973
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHL575273
2nd rowIS232033
3rd rowPN238558
4th rowHL575273
5th rowIS232033
ValueCountFrequency (%)
mc46946 259
 
2.2%
au364101 259
 
2.2%
jn355280 259
 
2.2%
je105477 259
 
2.2%
nu627706 259
 
2.2%
mf629602 259
 
2.2%
kt52578 259
 
2.2%
jn722010 259
 
2.2%
db583216 259
 
2.2%
dl460618 259
 
2.2%
Other values (263) 9401
78.4%
2025-02-07T12:54:36.734778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 9103
 
9.7%
2 7907
 
8.4%
4 7808
 
8.3%
7 7595
 
8.1%
1 7176
 
7.6%
8 6787
 
7.2%
5 6505
 
6.9%
3 6083
 
6.5%
0 5672
 
6.0%
9 5078
 
5.4%
Other values (21) 24259
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 9103
 
9.7%
2 7907
 
8.4%
4 7808
 
8.3%
7 7595
 
8.1%
1 7176
 
7.6%
8 6787
 
7.2%
5 6505
 
6.9%
3 6083
 
6.5%
0 5672
 
6.0%
9 5078
 
5.4%
Other values (21) 24259
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 9103
 
9.7%
2 7907
 
8.4%
4 7808
 
8.3%
7 7595
 
8.1%
1 7176
 
7.6%
8 6787
 
7.2%
5 6505
 
6.9%
3 6083
 
6.5%
0 5672
 
6.0%
9 5078
 
5.4%
Other values (21) 24259
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 9103
 
9.7%
2 7907
 
8.4%
4 7808
 
8.3%
7 7595
 
8.1%
1 7176
 
7.6%
8 6787
 
7.2%
5 6505
 
6.9%
3 6083
 
6.5%
0 5672
 
6.0%
9 5078
 
5.4%
Other values (21) 24259
25.8%
Distinct232
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size93.8 KiB
Minimum2003-01-16 00:00:00
Maximum2005-06-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-07T12:54:36.876039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:37.051551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

amount
Real number (ℝ)

Distinct273
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43562.704
Minimum615.45
Maximum120166.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.8 KiB
2025-02-07T12:54:37.213569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum615.45
5-th percentile6419.84
Q126155.91
median39580.6
Q352151.81
95-th percentile111654.4
Maximum120166.58
Range119551.13
Interquartile range (IQR)25995.9

Descriptive statistics

Standard deviation27278.616
Coefficient of variation (CV)0.62619199
Kurtosis1.1211273
Mean43562.704
Median Absolute Deviation (MAD)13424.69
Skewness1.0977008
Sum5.2236038 × 108
Variance7.4412291 × 108
MonotonicityNot monotonic
2025-02-07T12:54:37.364792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35420.74 259
 
2.2%
49539.37 259
 
2.2%
65071.26 259
 
2.2%
116208.4 259
 
2.2%
59830.55 259
 
2.2%
46895.48 259
 
2.2%
36140.38 259
 
2.2%
36251.03 259
 
2.2%
40206.2 259
 
2.2%
63843.55 259
 
2.2%
Other values (263) 9401
78.4%
ValueCountFrequency (%)
615.45 32
0.3%
1128.2 8
 
0.1%
1491.38 32
0.3%
1627.56 8
 
0.1%
1676.14 7
 
0.1%
1679.92 10
 
0.1%
1834.56 25
0.2%
1960.8 23
0.2%
2434.25 34
0.3%
2611.84 43
0.4%
ValueCountFrequency (%)
120166.58 259
2.2%
116208.4 259
2.2%
111654.4 180
1.5%
105743 43
 
0.4%
101244.59 180
1.5%
85559.12 41
 
0.3%
85410.87 180
1.5%
85024.46 29
 
0.2%
83598.04 180
1.5%
82261.22 55
 
0.5%

Interactions

2025-02-07T12:54:30.375822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.219525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.187199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.184831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.047027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.809021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.521377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.380738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:30.525610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.419635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.278280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.276536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.132637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.898145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.629581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.513306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:30.864335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.513544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.393935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.364140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.235899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.994994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.740409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.619555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:31.010056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.661381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.513620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.464880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.332750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.093486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.860453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.743484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:31.112627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.800850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.604553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.563614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.418535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.187957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.963579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.857083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:31.218737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.897034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.715364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.650907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.505461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.261128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.065155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.989759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:31.328637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:24.992412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.812376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.746801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.606026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.350219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.176721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:30.128907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:31.447747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.097929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:25.900491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:26.955609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:27.698573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:28.435156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:29.279709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-07T12:54:30.255934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-07T12:54:37.464808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
EmployeeNumberamountcustomerNumberorderLineNumberorderNumberoriginpriceEachquantityOrderedsales_amountstatus
EmployeeNumber1.000-0.1210.262-0.0220.0401.000-0.018-0.020-0.0230.122
amount-0.1211.000-0.3320.0870.0770.188-0.0040.0190.0020.072
customerNumber0.262-0.3321.000-0.048-0.0730.438-0.006-0.010-0.0010.126
orderLineNumber-0.0220.087-0.0481.000-0.0390.0190.018-0.033-0.0040.071
orderNumber0.0400.077-0.073-0.0391.0000.158-0.0050.0430.0180.356
origin1.0000.1880.4380.0190.1581.0000.0700.0340.0370.056
priceEach-0.018-0.004-0.0060.018-0.0050.0701.0000.0190.8290.079
quantityOrdered-0.0200.019-0.010-0.0330.0430.0340.0191.0000.5460.193
sales_amount-0.0230.002-0.001-0.0040.0180.0370.8290.5461.0000.119
status0.1220.0720.1260.0710.3560.0560.0790.1930.1191.000

Missing values

2025-02-07T12:54:31.592699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T12:54:31.815176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

orderNumberorderLineNumbercustomerNumberEmployeeNumberproductCodestatusquantityOrderedpriceEachsales_amountorigincomplete_order_numbercheckNumberpaymentDateamount
01010013631216S24_3969Shipped4935.291729.21spain10100-1HL5752732004-11-1750799.69
11010013631216S24_3969Shipped4935.291729.21spain10100-1IS2320332003-01-1610223.83
21010013631216S24_3969Shipped4935.291729.21spain10100-1PN2385582003-12-0555425.77
31010023631216S18_2248Shipped5055.092754.50spain10100-2HL5752732004-11-1750799.69
41010023631216S18_2248Shipped5055.092754.50spain10100-2IS2320332003-01-1610223.83
51010023631216S18_2248Shipped5055.092754.50spain10100-2PN2385582003-12-0555425.77
61010033631216S18_1749Shipped30136.004080.00spain10100-3HL5752732004-11-1750799.69
71010033631216S18_1749Shipped30136.004080.00spain10100-3IS2320332003-01-1610223.83
81010033631216S18_1749Shipped30136.004080.00spain10100-3PN2385582003-12-0555425.77
91010043631216S18_4409Shipped2275.461660.12spain10100-4HL5752732004-11-1750799.69
orderNumberorderLineNumbercustomerNumberEmployeeNumberproductCodestatusquantityOrderedpriceEachsales_amountorigincomplete_order_numbercheckNumberpaymentDateamount
1198110425101191370S18_2432In Process1948.62923.78spain10425-10NG946942005-02-2249523.67
1198210425111191370S32_1268In Process4183.793435.39spain10425-11DB9337042004-11-1419501.82
1198310425111191370S32_1268In Process4183.793435.39spain10425-11LN3734472004-08-0847924.19
1198410425111191370S32_1268In Process4183.793435.39spain10425-11NG946942005-02-2249523.67
1198510425121191370S10_4962In Process38131.494996.62spain10425-12DB9337042004-11-1419501.82
1198610425121191370S10_4962In Process38131.494996.62spain10425-12LN3734472004-08-0847924.19
1198710425121191370S10_4962In Process38131.494996.62spain10425-12NG946942005-02-2249523.67
1198810425131191370S18_4600In Process38107.764094.88spain10425-13DB9337042004-11-1419501.82
1198910425131191370S18_4600In Process38107.764094.88spain10425-13LN3734472004-08-0847924.19
1199010425131191370S18_4600In Process38107.764094.88spain10425-13NG946942005-02-2249523.67